AIMC Topic: Autism Spectrum Disorder

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A deep learning model for diagnosing autism using brain time series.

Neuroscience
The early identification of autism is especially critical as it can significantly enhance the effectiveness of intervention strategies. However, the recognition task remains challenging due to the subtle differences between ASD patients and neurotypi...

Enhancing theory of mind in autism through humanoid robot interaction in a randomized controlled trial.

Scientific reports
Autism Spectrum Disorder presents significant challenges in social cognition, particularly in understanding others' thoughts, emotions, and intentions. Traditional interventions often rely on role-playing games with human therapists or inanimate obje...

ASD-GraphNet: A novel graph learning approach for Autism Spectrum Disorder diagnosis using fMRI data.

Computers in biology and medicine
Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition with heterogeneous symptomatology, making accurate diagnosis challenging. Traditional methods rely on subjective behavioral assessments, often overlooking subtle neural biomarke...

MDNCT: a multi-domain neurocognitive transformer architecture approach for early prediction of autism spectrum disorders.

Scientific reports
Intellectual disability (ID) refers to a disorder involving intelligence and adaptive behavior that meets specific criteria involving deviance from the norm in terms of degree. ID is more common in males than females, and the causes can be genetic or...

Deep learning diagnosis plus kinematic severity assessments of neurodivergent disorders.

Scientific reports
Early diagnostic assessments of neurodivergent disorders (NDD), remains a major clinical challenge. We address this problem by pursuing the hypothesis that there is important cognitive information about NDD conditions contained in the way individuals...

Neur-Ally: a deep learning model for regulatory variant prediction based on genomic and epigenomic features in brain and its validation in certain neurological disorders.

NAR genomics and bioinformatics
Large-scale quantitative studies have identified significant genetic associations for various neurological disorders. Expression quantitative trait locusĀ (eQTL) studies have shown the effect of single-nucleotide polymorphisms (SNPs) on the differenti...

Air Pollution and Autism Spectrum Disorder: Unveiling Multipollutant Risks and Sociodemographic Influences in California.

Environmental health perspectives
BACKGROUND: Autism spectrum disorder (ASD) is a neurodevelopmental condition with increasing prevalence worldwide. Air pollution may be a major contributor to the rise in ASD cases. This study investigated how the risk of ASD associated with prenatal...

An interpretable deep learning approach for autism spectrum disorder detection in children using NASNet-mobile.

Biomedical physics & engineering express
Autism spectrum disorder (ASD) is a multifaceted neurodevelopmental disorder featuring impaired social interactions and communication abilities engaging the individuals in a restrictive or repetitive behaviour. Though incurable early detection and in...

The Gut Microbiota in Young Adults with High-Functioning Autism Spectrum Disorder and Its Performance as Diagnostic Biomarkers.

Nutrients
Diagnosing ASD in adults presents unique challenges, and there are currently no specific biomarkers for this condition. Most existing studies on the gut microbiota in ASD are conducted in children; however, the composition of the gut microbiota in c...

Topology-Guided Graph Masked Autoencoder Learning for Population-Based Neurodevelopmental Disorder Diagnosis.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Exploring the pathogenic mechanisms of brain disorders within population is an important research in the field of neuroscience. Existing methods either combine clinical information to assist analysis or use data augmentation for sample expansion, ign...